Environmental Management Zone Modeling And Analysis

ABSTRACT

Methods and systems for crop management are disclosed. An example method can comprise receiving first information associated with an environmental management zone. The first information can relate to one or more of a land characteristic and a management practice. The first information can comprise a soil type of the environmental management zone. An example method can comprise, receiving historical weather data relating to the environmental management zone. An example method can comprise receiving real-time weather data relating to the environmental management zone. An example method can comprise executing a growth model to predict a nitrogen range for the environmental management zone based on one or more of the first information, the historical weather data, and the real-time weather data. The nitrogen range can comprise probabilities for one or more of a current time period and a future time period in the growing season.

CROSS REFERENCE TO RELATED PATENT APPLICATION

This application claims priority to U.S. Provisional Application No.61/944,436 filed Feb. 25, 2014, U.S. Provisional Application No.62/019,175 filed Jun. 30, 2014, U.S. Provisional Application No.62/019,159 filed Jun. 30, 2014, U.S. Provisional Application No.62/039,283 filed Aug. 19, 2014, and U.S. Provisional Application No.62/039,286 filed Aug. 19, 2014, herein incorporated by reference intheir entirety.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Provided are methods and systemsfor land management. An example method can comprise receiving, by acomputing device, first information associated with an environmentalmanagement zone. The first information can relate to one or more of aland characteristic and a management practice. The first information cancomprise a soil type of the environmental management zone. An examplemethod can comprise receiving, by the computing device, historicalweather data relating to the environmental management zone. An examplemethod can also comprise receiving, by the computing device, real-timeweather data relating to the environmental management zone. An examplemethod can further comprise executing a growth model to predict anitrogen range for the environmental management zone based on one ormore of the first information, the historical weather data, and thereal-time weather data. The nitrogen range can comprise probabilitiesfor one or more of a current time period and a future time period in thegrowing season.

In another aspect, an example method can comprise receiving, by acomputing device, first information associated with an environmentalmanagement zone, wherein the first information relates to one or more ofa land characteristic and a first management plan. The first informationcan comprise a soil type of the environmental management zone. Anexample method can comprise receiving, by the computing device,historical weather data relating to the environmental management zone.An example method can comprise receiving, by the computing device,real-time weather data relating to the environmental management zone. Anexample method can also comprise generating a first future soil nitrogenavailability for the environmental management zone based on one or moreof the first information, the historical weather data, and the real-timeweather data. An example method can further comprise generating a firstrisk profile of yield-limiting soil nitrogen levels based upon at leastthe predicted first future soil nitrogen availability.

In yet another aspect, an example method can comprise receiving, by acomputing device, first information associated with an environmentalmanagement zone. The first information can relate to one or more of aland characteristic and a management practice. The first information cancomprise a soil type of the environmental management zone. An examplemethod can comprise receiving, by the computing device, historicalweather data relating to the environmental management zone. An examplemethod can comprise receiving, by the computing device, real-timeweather data relating to the environmental management zone. An examplemethod can comprise generating a nitrogen outcome probability for theenvironmental management zone based on one or more of the firstinformation, the historical weather data, and the real-time weather datafor a particular time period. An example method can also comprisereceiving, by the computing device, second information associated withan environmental management zone. The second information can comprise achange to the received first information. An example method can furthercomprise updating the nitrogen outcome probability based on the receivedsecond information.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a chart illustrating factors affecting crop growth andnitrogen availability in the crop and soil model;

FIG. 2 is a graph illustrating modeled and measured soil NO3-concentration;

FIG. 3A illustrates an example standard soil map;

FIG. 3B illustrates an example high resolution zone soil map;

FIG. 4 is a diagram illustrating an example system in which the presentsmethods and systems can operate;

FIG. 5A is a diagram illustrating a first pre-season planning field map;

FIG. 5B is a diagram illustrating a second pre-season planning fieldmap;

FIG. 5C is a diagram illustrating a third pre-season planning field map;

FIG. 6 is a diagram illustrating a pre-side-dress environmentalmanagement zone based field map showing the risk of yield-limiting soilnitrogen at V6 corn growth stage;

FIG. 7 is a graph illustrating a soil nitrogen forecast for a singleenvironmental management zone based on 50 years of historical weather;

FIG. 8 is a diagram illustrating an example user interface;

FIG. 9 is a diagram illustrating another example user interface;

FIG. 10 is a diagram illustrating another example user interface;

FIG. 11 is a diagram illustrating another example user interface;

FIG. 12 is a diagram illustrating another example user interface;

FIG. 13 is a diagram illustrating another example user interface;

FIG. 14 is a diagram illustrating another example user interface;

FIG. 15 is a diagram illustrating another example user interface;

FIG. 16 is a diagram illustrating another example user interface;

FIG. 17 is a block diagram illustrating an example system for landmanagement;

FIG. 18 is a flowchart illustrating an example method for landmanagement;

FIG. 19 is a flowchart illustrating another example method for landmanagement;

FIG. 20 is a flowchart illustrating yet another example method for landmanagement; and

FIG. 21 is a block diagram illustrating an example computing device inwhich the present methods and systems can operate.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described, itis to be understood that the methods and systems are not limited tospecific methods, specific components, or to particular implementations.It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting.

As used in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. Ranges may be expressed herein as from “about” oneparticular value, and/or to “about” another particular value. When sucha range is expressed, another embodiment includes from the oneparticular value and/or to the other particular value. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms anotherembodiment. It will be further understood that the endpoints of each ofthe ranges are significant both in relation to the other endpoint, andindependently of the other endpoint.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as “comprising” and“comprises,” means “including but not limited to,” and is not intendedto exclude, for example, other components, integers or steps.“Exemplary” means “an example of” and is not intended to convey anindication of a preferred or ideal embodiment. “Such as” is not used ina restrictive sense, but for explanatory purposes.

Disclosed are components that can be used to perform the disclosedmethods and systems. These and other components are disclosed herein,and it is understood that when combinations, subsets, interactions,groups, etc. of these components are disclosed that while specificreference of each various individual and collective combinations andpermutation of these may not be explicitly disclosed, each isspecifically contemplated and described herein, for all methods andsystems. This applies to all aspects of this application including, butnot limited to, steps in disclosed methods. Thus, if there are a varietyof additional steps that can be performed it is understood that each ofthese additional steps can be performed with any specific embodiment orcombination of embodiments of the disclosed methods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

Embodiments of the methods and systems are described below withreference to block diagrams and flowchart illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

Nitrogen is an important crop production input and can be one of themajor mineral nutrients that farmers manage to achieve high yields.Nitrogen management, however, can be among the most complex anduncertain agronomic aspects of modern farming. Large quantities oforganic nitrogen can be present in soils, but a small and highlyvariable fraction of this nitrogen is mineralized each growing seasoninto inorganic forms that are available for crop uptake. Inorganicnitrogen, originating from the soil or from fertilizers, can be readilyavailable to crops, but can be highly susceptible to losses. To add tothis complexity, all of the processes that control nitrogen availabilityand loss vary by soil type, moisture, temperature and other factors.Because of the multifaceted and dynamic nature of nitrogen, farmersregularly over-fertilize and/or under-fertilize in any given field oryear, reducing profitability and/or leading to excess nitrogenapplication. For example, farmers might apply nitrogen at rates inexcess of what is optimal in years when climatic conditions support highlevels of soil nitrogen mineralization. Farmers often apply higher thanminimally sufficient (e.g., optimal) nitrogen rates because the farmersdo not know in advance how much soil nitrogen mineralization might occurin any given year. While the economic penalty resulting from inadequatefertilization can be generally much greater than the cost of excessfertilizer, high fertilizer application rates can be associated with airand water pollution. Given the limited information available to makedecisions today, extra nitrogen application is often the economicallyoptimal nitrogen risk management strategy. In one aspect, the presentmethods and system can provide users, such as farmers, with new insightsto help the users make timelier and higher resolution nitrogenmanagement decisions that can better protect profitability andenvironmental quality. Further, the present methods and systems can beadapted for use with additional crop inputs, such as phosphorous,potassium, organic matter, lime, seed planting density, amount ofirrigation, and/or the like.

In one aspect, farmers can be provided with a new ability to plan,monitor and adapt nitrogen management practices to maximizeprofitability and improve environmental quality in the face of weatheruncertainty. The present methods and systems can comprise models, suchas growth models. Example growth models can comprise crop and nitrogenmodels, with high-resolution soil and/or weather data. The models can beused to dynamically forecast soil nitrogen status, providing farmerswith essentially real time probabilistic outcomes of their nitrogenlevels. One or more computers, such as a cloud-based software platform,may be used to implement the model or models to deliver the results in areal time manner. In one aspect, information regarding probabilisticdeterminations can be calculated to allow for monitoring and managementof soil nitrogen in real-time and for year round planning, which can becalculated on an operational, field, sub-field and/or environmentalmanagement zone basis. Alternatively, or additionally, analysis can beconducted at a high resolution spatial-scale by using environmentalmanagement zones. The environmental management zones can be based onsoil type, topography, hydrology, or some combination of these,classified based on zones that perform in a manner similar to each otherbased on one or more given environmental factor or factors, such asNitrogen depletion rate due to water flow. In another aspect, thepresent methods and systems can be configured to reclassify zone typesand/or areas in order to better account for the environmental managementzone based on a comparison of the models actual yield data and predictedyield data in seasons.

In one aspect, the present methods and systems can comprise and/or beimplemented with one or more models, such as a growth model. An examplegrowth model can comprise a crop and/or soil model configured tosimulate all aspects of crop growth and development, including grainyield at harvest, and/or the like for a specific set of soilcharacteristics and under defined management and weather conditions. Inone aspect, an example model can directly account for, predict, and/orthe like changes in soil water and nitrogen that occur over time inresponse to crop growth, management and weather (e.g., FIG. 1). FIG. 1is chart illustrating factors affecting crop growth and nitrogenavailability in the crop and soil model.

In one aspect the present methods and systems can comprise one or moremodels, such as a Crop Environment Resource Synthesis (CERES) maizemodel, CENTURY model, and/or the like. These models can be adapted todynamically use high-density weather data, updated on a daily basis orreal-time basis, to reforecast soil nitrogen for the environmentalmanagement zone. In one aspect, a network of weather stations, includingthose positioned on or near the farms where the environmental managementzone is located, may be used. For example, weather data can becollected, such as historical weather information specific to aparticular area, such as a field boundary, environmental managementzone, and/or the like.

The dynamic nature of the present methods and systems, combined withhigh density weather data, can enable a user to monitor changes in soilnitrogen status and assess the impacts of potential management actionsin real time. The present methods and systems can be implemented acrossa broad geographic range while incorporating soil, weather andmanagement information for specific fields. The present methods andsystems can be tested and adapted to predict nitrogen availabilityacross a wide range of soil, climatic and management conditions (e.g.,FIG. 2).

FIG. 2 is a graph illustrating modeled and measured soil NO3-concentration to a 12″ depth for three soils in fields. In one aspect,the one or more models can account for nitrogen in various forms,including anhydrous ammonia and/or manure fertilizer. In another aspect,the output of the one or more models can be provided to a machine forthe automated application of the suggested rate of the nitrogen source.

In one aspect, the present methods and systems can use soil maps, suchas high resolution soil maps. In one aspect, environmental managementzones can be generated using a machine learning clustering algorithmthat reclassifies the spatial distribution of soil properties withinfields based on digital elevation data, such as high resolutionelevation data. These environmental management zones can be identifiedbased on hydrological units or water shed basin. In one aspect, soiltype ranges found in the water shed basin in which the environmentalmanagement zone is located can be based on the soil characteristics ofat least one of soil density, sand-silt-clay value (e.g., percentages orlevels of one or more of sand, silt, and clay in the soil), waterholding capacity, and/or the like. As an example, water holding capacitycan comprise a wilting point, saturation point, and/or the like. Thewilting point can comprise the minimum amount of water the soil willhold. The saturation point can comprise the maximum amount of water thesoil will hold. Using these soil type ranges can result in a moreprecise soil map that better reflects field-scale hydrologicalattributes that strongly influence crop growth and nitrogen availability(e.g., as shown in FIG. 3A, FIG. 3B). In some implementations, anenvironmental management zone can be supplemented by direct measurementof soil chemical, physical and biological properties using laboratory ordirect sensing techniques. In other implementations, the environmentalmanagement zones can be recalibrated over time to optimize the abilityof the model to predict yield potential based on a set of crop inputs.FIG. 3A and FIG. 3B are example soil maps illustrating a comparison of astandard soil map to a high resolution soil map. FIG. 3A illustrates anexample standard soil map. FIG. 3B illustrates an example highresolution zone soil map.

In one aspect, the one or more models can be computer implemented. Inone another aspect, the present methods and systems can use a cloudcomputing framework to manage weather, soil and operational data fore.g., one or more environmental management zones on a regular basis(e.g., hourly, daily, weekly, monthly, yearly), while also giving usersaccess to real-time soil nitrogen status updates via applications, suchas web browsers and mobile applications (e.g., as shown in FIG. 4). FIG.4 is a diagram illustrating an example system in which the presentsmethods and systems can operate. In one aspect, users can have theoption to automatically share information (e.g., graphs, charts,predictions, management plans) and suggestions generated by the servicewith third parties involved in management of the field. In scenarioswhere the one or more model operates from one or more remote computingdevices, changes made to the one or more models and related software(e.g., soil mapping software) may be delivered to users withoutsubstantially interruption of service.

In one aspect, the present methods and systems can be configured to helpusers plan, monitor and adapt management practices, such as nitrogenmanagement practices, to help maximize profitability in the face ofclimatic uncertainty. The present methods and systems can be regularlyused from fall through spring, as well as during other times andseasons, to assist in the development of management plans. Thesemanagement plans can comprise guidance regarding the least-risk datesfor targeting nitrogen applications, as well as the overall risk thatyields will be limited by nutrient availability (e.g., nitrogenavailability) given historic weather conditions at the location. In oneaspect, with just a keystroke, a user can use the present methods andsystems to access soil nutrient forecasts for any management plan theuser has created and compare the current risk profile of the managementplan against alternative management scenarios (e.g., FIG. 5A, FIG. 5B,FIG. 5C), or generate a variable rate suggestion for in-season nutrientapplication at any future date (e.g., FIG. 6). For example, nutrientapplication can comprise an amount applied, a form of application,and/or a timing of application of a nutrient, such as least one ofnitrogen, phosphorus, potassium, lime, water, and/or the like.

In one aspect, the present methods and systems can use data of previousweather years to enables dynamic and risk-based management planning.When a user creates or accesses a management plan, the model can be runfrom a remote device (e.g. from the cloud) to simulate soil nutrientavailability from that date forward based on each of the previous yearsof weather data. Risk-based management planning can also use weatherand/or climate forecast models in addition to or instead of priorweather data. In one aspect, 5 or more years of weather data can beused. In another aspect, 10 or more years of weather data can be used.In another aspect, 15 or more years of weather data can be used. Inanother aspect, 20 or more years of weather data can be used. In anotheraspect, 25 or more years of weather data can be used. In another aspect,30 or more years of weather data can be used. In another aspect, 35 ormore years of weather data can be used. In another aspect, 40 or moreyears of weather data can be used. In another aspect, 45 or more yearsof weather data can be used. In another aspect, 50 years of weather datacan be used. In another aspect, 50 or more years of weather data can beused. In another aspect, years with similar weather patterns can beused. As a result of the use of a range of possible future weatheroutcomes based on probabilities calculated from historical data, the oneor more models can estimate the risk of yield-limiting soil nutrientlevels, while also factoring in observed weather and agriculturalpractices and nutrient application to the current date (e.g., FIG. 7).By simulating a wide range of possible weather outcomes, the model canprovide users the ability to assess and compare the financial risksassociated with different management plans, and to make quantitative,risk-based management decisions in real-time.

FIG. 5A is a diagram illustrating a first pre-season planning field map.FIG. 5B is a diagram illustrating a second pre-season planning fieldmap. FIG. 5C is a diagram illustrating a third pre-season planning fieldmap. The pre-season planning maps show risk of yield-limiting soilnitrogen at V6 (pre-side-dress) corn growth stage for three differentnitrogen management plans forecasted for the same field on Nov. 1, 2013.FIG. 6 is a diagram illustrating a pre-side-dress environmentalmanagement zone based field map showing the risk of yield-limiting soilnitrogen at V6 (pre-side-dress) corn growth stage, and ability togenerate variable rate nitrogen suggestions. FIG. 7 is a graphillustrating soil nitrogen forecast for a single environmentalmanagement zone based on 50 years of historical weather.

In one aspect, users can access the software components of the one ormore models via a user interface, such as a web interface (e.g., asshown in FIG. 8-FIG. 16). The user interface can be connected directlyto other agronomic, business, and/or logistical services.

In one aspect, the user interface can be configured to provide access toone or more of the following features: 1) real-time soil nitrogen levelsfor the currently selected field and/or environmental management zone;2) historical crop, yield and management information for the currentlyselected field and/or environmental management zone; 3) a map of thecurrently selected field with real-time soil nutrient (e.g., nitrogen)levels displayed for each environmental management zone within thefield; 4) drop-down menus allowing users to quickly navigate betweenfields; 5) a nutrient planning tool that allows users to enterapplication dates, methods and quantities to generate a plan for thecurrently selected field; 6) a risk profile for the currently selectedplan and field; 7) a link to calendar tool for planning targetapplication dates (e.g., for providing the nutrient to specified areasof an environmental management zone), and/or the like.

FIG. 8 is a diagram illustrating an example user interface 800. The userinterface can comprise a variety of user interface elements configuredto provide information. For example, the user interface elements canshow, current soil nutrient status 802, field history 804, field mapwith environmental response units 806 (e.g., management zones), anavigation element 808 for selecting various farms and fields, anutrient (e.g., nitrogen) plan for a current crop 810, a predicted riskprofile for display risks of inadequate nutrients 812, a calendar 814illustrating plans for applying nutrient and other management practices,and/or the like.

FIG. 9 is a diagram illustrating another example user interface 900. Theuser interface 900 can show nutrient levels 902, 904, 906 based onenvironmental management zones, or as shown in FIG. 9, on larger areas,made up of several environmental management zones. FIG. 10 is a diagramillustrating another example user interface 1000. In one aspect, theuser interface 1000 can be configured to display one or moreprobabilities (e.g., predictions) 1002, 1004, 1006.

FIG. 11 is a diagram illustrating another example user interface 1100.The user interface 1100 can be configured to show a graph 1102 of a stepdown, or decrease of nutrients, at the plant V6 stage of development.The graph 1102 can display nitrogen levels 1104 over time for amanagement plan. In one aspect, the user interface can comprise a sliderbar 1106. The slider bar 1106 can be moved left and right to select apredicted range of nutrient levels (e.g., nitrogen levels) at differentpoints in time. For example, the slider bar 1106 can be configured toselect one or more days of the year. Information related to the selectedone or more days can be displayed. As an example, the displayedinformation can comprise risk profiles 1108 indicative of predictednutrient levels. For example, a risk profile 1108 can comprise one ormore probabilities that nutrient levels will be within a certain riskcategory (e.g., safe, warning, danger). One or more (or each) nutrientlevel point of the graph 1102 can be calculated based on a probabilityof outcomes generated based on actual in season data (e.g., plant growthstage, precipitation since planting, accumulated growing degree days),soil type determined by environmental response zone, high resolutionhistorical weather data for the zone, and/or the like.

FIG. 12 is a diagram illustrating another example user interface 1200.This user interface 1200 can show risk profiles for a primary managementplan and for an alternative management plan based on historical weatherdata and actual management practices to date. In certain scenarios, thealternative management plan can suggest a greater financial benefitwhile maintaining or enhancing yield in comparison to the primarymanagement plan. As an example, the primary management plan can be basedupon a user's hypothetical planned practice. FIG. 13 is a diagramillustrating another example user interface 1300. The user interface1300 can show a probabilistic nutrient outcome 1302, 1304, 1306 based onpredicted weather determined in part with historical weather data. Theuser interface 1300 can illustrate different nutrient levels predictedwithin different adjacent environmental management zones.

FIG. 14 is a diagram illustrating another example user interface 1400.The user interface 1400 can be configured to show predictions (e.g.,based on the one or more models) of nutrient levels 1402 and riskprofiles 1404 for a time period immediately prior to the V6 stage ofplant growth. For example, the user interface 1400 can comprise a graph1406 configured to display ranges of predicted nitrogen levels for eachday. FIG. 15 is a diagram illustrating another example user interface1500. The user interface 1500 can be configured to show predictions(e.g., based on the one or more models) of nutrient levels 1502 and riskprofiles 1504 for a time period immediately prior to the VT stage ofplant growth. The user interface 1500 can be used to assess differencesbetween predicted nutrient levels and risk profiles at other times, suchas prior to the V6 stage as shown in FIG. 15. FIG. 16 is a diagramillustrating another example user interface 1600. The user interface1600 can be configured to display a settings page that allows a user toenter costs of nutrients 1602, (e.g., nitrogen, fertilizer, water),application costs 1604, and/or the like which can be used to calculatefinancial profit or loss and to calculate risk estimate.

FIG. 17 is a block diagram illustrating an example system 1700 for landmanagement. For example, the system 1700 can be configured to providepredictions and other analysis regarding land conditions, such as thepresence of a substance in soil.

In one aspect, the system 1700 can comprise a management device 1702configured to receive and provide information related to land, soil,crops, weather, and/or the like through a network 1704. In one aspect,the network 1704 can comprise a packet switched network (e.g., internetprotocol based network), a non-packet switched network (e.g., quadratureamplitude modulation based network), and/or the like. The network 1704can comprise network adapters, switches, routers, modems, and the likeconnected through wireless links (e.g., radio frequency, satellite)and/or physical links (e.g., fiber optic cable, coaxial cable, Ethernetcable, or a combination thereof). In one aspect, the network 1704 can beconfigured to provide communication from telephone, cellular, modem,and/or other electronic devices to and throughout the system 1700.

In one aspect, the management device 1702 can comprise an informationunit 1706 configured to store information. For example, the informationunit 1706 can comprise a database, table, file, and/or the like. In oneaspect, information can comprise data indicative of and/or associatedwith an environmental management zone (e.g., farm management zone). Anenvironmental management zone can be subdivided into a plurality ofportions with associated data for one or more (or each) of the portions.For example, the information can comprise physical boundaries, elevationdata, ownership data, planting data, soil data, nutrient data (e.g.,nitrogen content), and/or the like information indicative of one or moreenvironmental management zones. The nutrient levels in differentportions of an environmental management zones can change even though thesame management plan is implemented across all areas of theenvironmental management zone. In another aspect, the information cancomprise weather data. The weather data can comprise historic weatherdata, real-time current weather data, future weather data (e.g.,predicted weather), and/or the like. For example, the system 1700 cancomprise a weather device 1708 configured to provide weather data. Themanagement device 1702 can request and/or receive the weather data fromthe weather device 1708. The weather device 1708 can comprise acomputing device managed by a weather service (e.g., weather station).The weather device 1708 can comprise one or more weather measuringinstruments, such as a barometer, humidity sensor, rain sensor,temperature sensor, and/or the like. For example, the weather data cancomprise satellite data, temperature data, rain levels, floodinginformation, and/or the like. The weather data can comprise timinginformation, location information, and/or the like that can beconfigured for correlating the weather data with the other information,such as environmental management zone data.

In one aspect, the information can comprise user data indicative of oneor more users. For example, the information can comprise usercredentials (e.g., login, password), account history, subscription data,user preferences, and/or the like. The user data can be associated withthe other portions of the information such that a user can accessenvironmental management data associated with the user. For example, anagent of a farm or other environmental management zone can be associatedwith information related to the environmental management zone.

In one aspect, the information can comprise management information, suchas a management plan, associated with one or more environmentalmanagement zones. A management plan can comprise plans for a variety formanagement practices, such as watering, applying nutrients (e.g.,nitrogen, fertilizer, phosphorus, potassium, lime, water), applyingpesticide, tilling, harvesting, plowing, planting, and/or the like. Forexample, the management plan can comprise location information, timinginformation indicating time and place that various portions of the plansare to be carried out. In one aspect, the management plan can beassociated with one or more users. For example, an environmentalmanagement zone can be associated with one or more management plans. Anenvironmental management zone can be associated with one or more users.

In one aspect, the management device 1702 can comprise an analysis unit1710 configured to process (e.g., analyze) information, such asinformation stored by information unit 1706. In one aspect, the analysisunit 1710 can be configured to determine one or more predictions (e.g.,predicted values) of future conditions of an environmental managementzone. The predictions can comprise data values, maximum predictedvalues, minimum predicted values, a predicted range for a value, and/orthe like. The analysis unit 1710 can determine the prediction of futureconditions based one or more models, such as growth models (e.g., plantgrowth models), soil models, weather models, and/or the like. Forexample, the analysis unit 1710 can determine predictions of one or moreranges, values, and/or the like of future weather for the environmentalmanagement zone. As an illustration, the predictions can comprise one ormore (e.g., a series of) probabilities for one or more of a current timeperiod and a future time period in the growing season. The analysis unit1710 can determine predictions of a variety of characteristics, such assoil conditions (e.g., pH level, percentage of organic matter), nutrientconditions (e.g., nitrogen content, phosphorus level, potassium level,fertilizer, lime level), moisture conditions (e.g., water holdingcapacity, amount of water in the soil), growth conditions (e.g., plantgrowth or yield), and/or the like. In one aspect, the predictions can bedetermined based on weather data (e.g., historical, real-time current,future), location data, elevation data, soil data, any other informationdescribed herein, and/or the like. For example, the analysis unit 1710can be configured to predict future weather based on historical weatherdata and/or real-time current weather data. As a further illustration, aprediction can comprise a predicted future soil nutrient (e.g.,nitrogen) availability, soil nutrient (e.g., nitrogen, fertilizer,phosphorus, potassium, lime, water) level, plant nutrient intake, and/orthe like.

In one aspect, the analysis unit 1710 can be configured to determine(e.g., generate, receive, calculate, access) one or more risk profilesfor a management plan. The risk profiles can be determined based on theone or more models. For example, the analysis unit 1710 can determine arisk profile of yield-limiting soil nutrient levels. The risk profilecan comprise probabilities that specific conditions will occur (e.g.,nitrogen levels will be low enough to limit plant growth yields) invarious portions of the environmental management zone. The risk profilecan comprise different probabilities for different portions of theenvironmental management zone. The risk profile can be determined basedupon the predicted future soil nutrient availability, soil nutrientlevel, plant nutrient intake, and/or the like.

In one aspect, the analysis unit 1710 can be configured to compareinformation. For example, the analysis unit 1710 can be configured tocompare management plans, risk profiles, predicted growth profile,and/or the like for one or more environmental management zones. Forexample, the analysis unit 1710 can determine one or more comparativemetrics to illustrate differences in predicted conditions of theenvironmental management zone.

In one aspect, the management device 1702 can comprise a first interfaceunit 1712 configured to manage user interactions with the managementdevice 1702. For example, the first interface unit 1712 can beconfigured to provide computer readable code configured to instruct auser device to render a user interface on a display. The user interfacecan comprise a variety of interface elements, such as windows, buttons,graphical elements (e.g., graphs, pictures), text boxes, text, and/orthe like. The user interface can be configured to display informationstored by the information unit 1706. For example, the first interfaceunit 1712 can provide computer readable code configured to retrieve theinformation from the information unit 1706. As an illustration, thecomputer readable code can be configured to display environmentalmanagement zone maps, risk profiles, management plans, predictedconditions, comparisons thereof, and/or the like.

In one aspect, the user interface can be configured to allow users togenerate management plans for an environmental management zones. Forexample, users can determine times, amounts, locations, and/or the liketo perform management practices, such as adding nutrients, harvesting,irrigating, tilling, aerating, applying pesticides, and/or the like. Themanagement plans can be specific to a plurality of sub-regions of anenvironmental management zone. The management plans can indicate whichmanagement practices to be performed on which days. The management planscan be updated by users and/or in response to threshold conditions beingmet.

In one aspect, the user interfaces can be configured to allow users tocontrol remote devices, such as a cultivation device 1714. A cultivationdevice 1714 can be a device configured to apply and/or scheduleapplications of procedure (e.g., tilling, weeding, aeration) and/orapplications of nutrient (e.g., nitrogen, fertilizer, phosphorus,potassium, lime, water). For example, the cultivation device 1714 can beconfigured to release an amount of a nutrient (e.g., nitrogen,fertilizer, phosphorus, potassium, lime, water) at a specified time andlocation within the environmental management zone. In one aspect, thecultivation device 1714 can be reconfigured to alter the amount ofnutrients applied, timing of nutrients applied, form of nutrientapplication, and/or the like. For example, the cultivation device 1714can receive updated application instructions from the management device1702. The instructions can be updated based on one or more predictionsof the one or more models.

In one aspect, the management device 1702 can comprise an update unit1716 configured to receive updated information. For example, the updateunit 1716 can periodically poll information sources, such as the weatherdevice 1708, and/or receive updates (e.g., alerts, notifications) atscheduled and/or unscheduled times. The update unit 1716 can beconfigured to provide the updated information to the information unit1706. For example, the update unit 1716 can be configured to determinechanges in the information and provide the change and/or the entireupdated data. As an illustration, the update unit 1716 can be configuredto receive a notification of a weather event, such as a storm, flood,tornado, drought, heat wave, cold snap, and/or the like. The update unit1716 can provide the updated information to the analysis unit 1710. Inresponse, the analysis unit 1710 can process the updated information andcan update other information such as one or more predictions of acondition of the environmental management zone, management plans,management suggestions, risk profiles, and/or the like.

In one aspect, the system 1700 can comprise one or more user devices1718. The user devices 1718 can be configured to provide content,services, information, applications, and/or the like to one or moreusers. For example, a user device 1718 can comprise a computer, a smartdevice (e.g., smart phone, smart watch, smart glasses, smart apparel,smart accessory), a laptop, a tablet, a display device (e.g.,television, monitor), digital streaming device, proxy, gateway,transportation device (e.g., on board computer, navigation system,vehicle media center), sensor node, and/or the like.

In one aspect, a user device 1718 can comprise a second interface unit1720 configured to provide an interface to a user to interact with theuser device 1718 and/or remote devices, such as the management device1702. The second interface unit 1720 can be any interface for presentingand/or receiving information to/from the user, such as user feedback. Anexample interface can comprise a content viewer, such as a web browser(e.g., Internet Explorer®, Mozilla Firefox®, Google Chrome®, Safari®, orthe like), media player, application (e.g., web application, smartdevice application), and/or the like. Other software, hardware, and/orinterfaces can be used to provide communication between the user and oneor more of the user device 1718 and the management device 1702. Thesecond interface unit 1720 can be configured to receive the computerreadable code from the first interface unit 1712 and render the userinterface described above to a user.

FIG. 18 is a flowchart illustrating an example method 1800 for landmanagement. At step 1802, first information associated with anenvironmental management zone can be received. An environmentalmanagement zone can comprise one or more plots of land, regions of land,properties, fields, crop growth locations, and/or the like. The firstinformation can be received by a computing device. The first informationcan relate to (e.g., comprise, describe, specify, identify, indicate)one or more of a land characteristic (e.g., soil type, topology,drainage) and a management practice (e.g., application information). Thefirst information can comprise a soil type of the environmentalmanagement zone. The first information associated with the environmentalmanagement zone can comprise application information related to at leastone of nitrogen, phosphorus, potassium, lime, water, and/or the like.The application information can comprise an amount applied, a form ofapplication, and/or a timing of application (e.g., day and time,pre-planting time, sidedress) of at least one of nitrogen, phosphorus,potassium, lime, and water. For example, alternative forms of nitrogenapplication can comprise application of alternative forms of a nutrient(e.g., a different material and/or chemical formulation comprising thenutrient) and/or use of different forms of application (e.g., surfaceapplication, injection, and/or the like).

At step 1804, historical weather data relating to (e.g., associatedwith, describing conditions within) the environmental management zonecan be received. The historical weather data can be received by thecomputing device. For example, the historical weather data can compriseweather data related to prior time periods, such as prior months,growing seasons, and/or the like.

At step 1806, real-time weather data relating to (e.g., associated with,describing conditions within) the environmental management zone can bereceived. The real-time weather data can be received by the computingdevice. Real-time weather data can comprise substantially currentweather data, such as weather data relating to a time window (e.g.,nominal time window). Example time windows for real-time weather datacan comprise 1 second, 10 seconds, 5 minutes, 30 minutes, 1 day, or anyother appropriate time window for a desired level of accuracy. Real-timeweather data can comprise weather data as the weather data is receivedfrom weather sensors and provided to the computing device.

At step 1808, a growth model can be executed (e.g., processed on acomputer processor) to predict a nitrogen range (e.g., or other nutrientrange) for the environmental management zone. The growth model can beexecuted to predict the nitrogen range based on one or more of the firstinformation, the historical weather data, the real-time weather data,and/or the like. For example, the nitrogen range can compriseprobabilities (e.g., a series of probabilities) for one or more of acurrent time period and a future time period in the growing season. Theprobabilities can comprise probabilities that a nutrient (e.g.,nitrogen) will be below a threshold (e.g., yield limiting level). Theprobabilities can be specific and/or correspond to a plurality ofdifferent portions of the environmental management zone. In an aspect,the probabilities for specific portions of the environmental managementzone can be time dependent, vary over time, and/or the like.

In an aspect, the nitrogen range can comprise a range of values that maybe interpreted relative to the Nitrogen content in the soil at thatlocation. The range of values can describe boundaries for the Nitrogenconcentration estimated to be present in a part of the soil (e.g.,located in the environmental management zone). The range of values cancomprise probabilities of the likelihood of different concentrationlevels. The range of values can comprise other values, such ascategorical values (e.g., “danger” and “safe”), that may indicatewhether the Nitrogen level (e.g., in the all or a part of theenvironmental management zone) is estimated to be sufficient for theusers' objectives or insufficient for the users' objectives.

In an aspect, executing the growth model to predict the nitrogen rangefor the environmental management zone can comprise forecasting futureweather conditions based on at least one of the real-time weather dataand the historical weather data.

In one aspect, the growth model can comprise a plant growth model. Forexample, the growth model can comprise the Crop Environment ResourceSynthesis (CERES) maize crop growth and development model. In someembodiments, the growth model can comprise the Decision Support Systemfor Agrotechnology Transfer (DSSAT) model. For example, the DSSAT modelmay be used when using the present methods and systems with a crop, suchas wheat. In one aspect, the growth model can predict the nitrogen rangefor the environmental management zone based on second information. Thesecond information can comprise one or more of seed planting date, seedplanting density, and seed variety attributes. For example, the secondinformation can relate to maize seed. In one aspect, the growth modelcan further calculate the difference between the optimum point of anitrogen response curve (e.g., or other nutrient response curve) and oneor more of an actual (e.g., measured) current soil rate of nitrogen(e.g., or other nutrient) and a predicted current soil rate of nitrogen(e.g., or other nutrient).

In one aspect, the growth model can be used to calculate an optimumpoint of a nitrogen response curve (e.g., or other nutrient responsecurve) based on the probabilities. In one aspect, the optimum point ofthe nitrogen response curve can be determined based upon the maximumyield per nitrogen. As another example, the optimum point of thenitrogen response curve can be determined based upon the maximum yieldper nitrogen (e.g., or other nutrient) adjusted for the economic cost ofone or more sources of nitrogen (e.g., or nutrient). As a furtherexample, the optimum point of the nitrogen response curve can bedetermined based upon the maximum yield per nitrogen (e.g., or othernutrient) adjusted to minimize runoff of excess nitrogen from theenvironmental management zone.

In an aspect, the growth model may be optimized for use with certaincrops. For example, when the growth model is optimized for corn, a soildepth profile of about 2 to 3 feet may be used. For a crop such aswheat, a soil depth profile of about 1 foot may be used in the growthmodel. For corn, a grain nitrogen removal rate of 0.8 pounds of nitrogenper bushel may be used in the model, while for wheat a higher rate ofabout 1.5 pounds of nitrogen per bushel can be used in the growth model.As a further example, a critical growth stage for corn, from a nitrogenperspective, can be from V6 to tillering. For wheat, the critical growthstage, from a nitrogen perspective, can be from Feekes 10 to 11. Acomparative relative maturity rate (CRM) rate can be used for corn inthe growth model. The growth model can use a lodging score whenoptimized for wheat. A growth model used for wheat can be adjusted totake into account that an over application of nitrogen on wheat canincrease the plants susceptibility to lodging with potentially largenegative yield effects.

At step 1810, the optimum point of nitrogen (e.g., or other nutrient)can be stored. For example, the optimum point can be stored as anelectronic data file and used as an input parameter for application ofnitrogen (e.g., or other nutrient). For example, the optimum point canbe used to control the application of a nitrogen source (e.g., or othernutrient source). For example, the optimum point can be used by anapplication machine to apply nitrogen to portions of the environmentalmanagement zone. Amounts and places to apply the nitrogen within theenvironmental management zone can be determined based on the optimumpoint.

FIG. 19 is a flowchart illustrating an example method 1900 for landmanagement. At step 1902, first information associated with anenvironmental management zone can be received. The first information canbe received by a computing device. The first information can relate to(e.g., comprise, describe, specify, identify, indicate) one or more of aland characteristic (e.g., soil type, topology, drainage) and a firstmanagement plan. The first information can comprise a soil type of theenvironmental management zone. The first information can furthercomprise one or more of seed planting date, seed planting density, seedvariety attributes, and/or the like. For example, the first informationcan relate to maize seed.

At step 1904, historical weather data relating to (e.g., associatedwith, describing conditions within) the environmental management zonecan be received. For example, the historical weather data can bereceived by the computing device. For example, the historical weatherdata can comprise weather data related to prior time periods, such asprior months, growing seasons, and/or the like.

At step 1906, real-time weather data relating to (e.g., associated with,describing conditions within) the environmental management zone can bereceived. For example, the real-time weather data can be received by thecomputing device. Real-time weather data can comprise substantiallycurrent weather data, such as weather data relating to a time window(e.g., nominal time window). Example time windows for real-time weatherdata can comprise 1 second, 10 seconds, 5 minutes, 30 minutes, 1 day, orany other appropriate time window for a desired level of accuracy.Real-time weather data can comprise weather data as it is received fromweather sensors and provided to the computing device.

At step 1908, a first future soil nitrogen availability (e.g., or firstfuture soil nutrient availability) for the environmental management zonecan be generated. For example, the first future soil nitrogenavailability for the environmental management zone can be generatedbased on one or more of the first information, the historical weatherdata, the real-time weather data, and the like. For example, generatingthe first future soil nitrogen availability for the environmentalmanagement zone can comprise forecasting future weather conditions, soilconditions, nutrient conditions, and/or the like based on at least oneof the real-time weather data and the historical weather data.

At step 1910, a first risk profile of yield-limiting soil nitrogenlevels (e.g., or other yield-limiting soil nutrient levels) can begenerated based upon at least the first future soil nitrogenavailability. For example, the first risk profile can comprise one ormore probabilities that nutrient levels (e.g., soil nitrogen levels)will be within a certain risk category (e.g., safe, warning, danger).The first risk profile can comprise probabilities that specificconditions will occur (e.g., nitrogen or other nutrient levels will below enough to limit plant growth yields) in various portions of theenvironmental management zone. The first risk profile can comprisedifferent probabilities for different portions of the environmentalmanagement zone. In an aspect, the probabilities for specific portionsof the environmental management zone can be time dependent and/or varyover time.

At step 1912, second information associated with the environmentalmanagement zone can be received. For example, the second information canbe received by the computing device. The second information can relateto (e.g., comprise, describe, specify, identify, indicate) one or moreof the land characteristic (e.g., or another land characteristic) and asecond management plan. For example, a user may provide the secondmanagement plan. The second management plan can specify different plansfor applying nutrients (e.g., different application times, differentamounts of nutrients to apply) than are specified in the firstmanagement plan. The present methods and systems can comprise a userinterface configured to compare risks associated with the firstmanagement plan and second management plan.

At step 1914, second future soil nitrogen availability (e.g., or secondfuture soil nutrient availability) for the environmental management zonecan be generated. For example, the second future soil nitrogenavailability for the environmental management zone can be generatedbased on one or more of the second information, the historical weatherdata, the real-time weather data, and/or the like.

At step 1916, a second risk profile of yield-limiting soil nitrogenlevels (e.g., or other yield-limiting soil nutrient levels) can begenerated based upon at least the second future soil nitrogenavailability. For example, the second risk profile can comprise one ormore probabilities that nutrient levels (e.g., soil nitrogen levels)will be within a certain risk category (e.g., safe, warning, danger).The second risk profile can comprise probabilities that specificconditions will occur (e.g., nitrogen or other nutrient levels will below enough to limit plant growth yields) in various portions of theenvironmental management zone. The second risk profile can comprisedifferent probabilities for different portions of the environmentalmanagement zone. In an aspect, the probabilities for specific portionsof the environmental management zone can be time dependent and/or varyover time.

At step 1918, the first risk profile and the second risk profile can beprovided for display. For example, the first risk profile and secondrisk profile can be provided (e.g., transmitted) to a local or remoteuser device (e.g., laptop, smart phone, tablet, workstation). As afurther example, a user can request the first risk profile, second riskprofile, and/or the like through a user interface, such as a farmmanagement interface. In an aspect, the user interface can be configuredto display the risk profile as a risk profile map. The risk profile mapcan associate and/or display portions of the land management zone with avariety of colors, symbols, hashing, and/or the like to illustrate thedifferent risks for different portions of the management zone.

At step 1920, the first risk profile and the second risk profile can bedisplayed concurrently for comparison. For example, the first riskprofile and the second risk profile can be displayed on the userinterface.

FIG. 20 is a flowchart illustrating an example method 2000 for landmanagement. At step 2002, first information associated with anenvironmental management zone can be received. For example, the firstinformation can be received by a computing device. The first informationcan relate to (e.g., comprise, describe, specify, identify, indicate)one or more of a land characteristic and a management practice. Thefirst information can comprise a soil type of the environmentalmanagement zone. The first information can comprise one or more of seedplanting date, seed planting density, seed variety attributes, and/orthe like. For example, the first information can relate to maize seed.

At step 2004, historical weather data relating to (e.g., associatedwith, describing conditions within) the environmental management zonecan be received. For example, the historical weather data can bereceived by the computing device. The historical weather data cancomprise weather data related to prior time periods, such as priormonths, growing seasons, and/or the like.

At step 2006, real-time weather data relating to (e.g., associated with,describing conditions within) the environmental management zone can bereceived. For example, the real-time weather data can be received by thecomputing device. Real-time weather data can comprise substantiallycurrent weather data, such as weather data relating to a time window(e.g., nominal time window). Example time windows for real-time weatherdata can comprise 1 second, 10 seconds, 5 minutes, 30 minutes, 1 day, orany other appropriate time window for a desired level of accuracy.Real-time weather data can comprise weather data as it is received fromweather sensors and provided to the computing device.

At step 2008, a nitrogen outcome probability (e.g., or other nutrientoutcome probability) for the environmental management zone can begenerated. For example, the nitrogen outcome probability for theenvironmental management zone can be generated based on one or more ofthe first information, the historical weather data, the real-timeweather data for a particular time period, and the like.

A nitrogen (e.g., or other nutrient) outcome probability can begenerated using the Crop Environment Resource Synthesis (CERES) maizecrop growth and development model. In one aspect, the nitrogen outcomeprobability can be generated based on a growth model configured tocalculate the difference between the optimum point of the nitrogen(e.g., or other nutrient) response curve and one or more of an actual(e.g., measured) current soil rate of nitrogen (e.g., or other nutrient)and a predicted current soil rate of nitrogen (e.g., or other nutrient).In another aspect, generating the nitrogen outcome probability for theenvironmental management zone can comprise forecasting future weatherconditions based on at least one of the real-time weather data and thehistorical weather data.

The nitrogen (e.g., or other nutrient) outcome probability can comprisea nitrogen (e.g., or other nutrient) response curve. For example,generating the nitrogen (e.g., or other nutrient) outcome probabilitycan comprise calculating an optimum point of a nitrogen response curve(e.g., or other nutrient response curve). The optimum point of thenitrogen response curve can be determined (e.g., calculated) based uponthe maximum yield per nitrogen (e.g., or maximum yield per nutrient).The optimum point of the nitrogen (e.g., or other nutrient) responsecurve can be determined based upon the maximum yield per nitrogen (e.g.,or maximum yield per nutrient) adjusted for the economic cost of one ormore sources of nitrogen (e.g., or other nutrient). The optimum point ofthe nitrogen response curve can be determined based upon the maximumyield per nitrogen (e.g., or maximum yield per nutrient) adjusted tominimize runoff of excess nitrogen (e.g., or other nutrient) from theenvironmental management zone.

At step 2010, second information associated with an environmentalmanagement zone can be received. For example, the second information canbe received by the computing device. The second information can comprisea change to the received first information. The second information cancomprise a change to the land characteristic, the management practice,and/or the like. The second information can comprise an updated soiltype of the environmental management zone. The second information cancomprise an update to one or more of the seed planting date, the seedplanting density, the seed variety attributes, and/or the like.

At step 2012, the nitrogen outcome probability (e.g., or other nutrientoutcome probability) can be updated based on the received secondinformation. For example, one or more of steps 2002, 2004, 2006, and2008 can be repeated with the second information. For example, thegrowth model can be executed based on the second information (e.g., orthe first information with the addition of the second information),historical weather data, the real-time weather data (e.g., real-timeweather data updated after the second information is received), and/orthe like to determine (e.g., calculate) an update to the nitrogenoutcome probability.

In an aspect, the nitrogen outcome probabilities can describe the chanceto have sufficient nitrogen at key growing times given a managementplan. The next step can comprise updating, revising, and/or the like themanagement plan so that the new management plan has a better chance ofachieving the grower's objectives (e.g., maximizing the likelihood ofsufficient nitrogen at critical growth stages given limited funds,limited time to put down applications, access to only a specific Nproduct, etc.)

It should be noted that methods 1800, 1900, and 2000 are not limited tothe use of a growth model as described above to determine or generatecharacteristics or probability values. Additionally, the growth modelsdescribed with methods 1800, 1900, and 2000 are not limited todetermining only the types of values and probabilities described above.For example, other characteristics and probabilities related to anenvironmental management zone can be determined by the growth modeland/or other models. As an illustration, an amount or level of nitrogen,phosphorus, potassium, organic matter, and/or the like for at least aportion of the environmental management zone can be determined and/orpredicted based on the growth model and/or other models. As a furtherillustration, a pH level, water holding capacity (e.g., wilting point,saturation point), and/or the like for at least a portion of anenvironmental management zone can be determined and/or predicted by thegrowth model and/or other models.

In an exemplary aspect, the methods and systems can be implemented on acomputer 2101 as illustrated in FIG. 21 and described below. By way ofexample, management device 1702, cultivation device 1714, weather device1708, and/or user device 1718 of FIG. 17 can be computers as illustratedin FIG. 21. Similarly, the methods and systems disclosed can utilize oneor more computers to perform one or more functions in one or morelocations. FIG. 21 is a block diagram illustrating an exemplaryoperating environment for performing the disclosed methods. Thisexemplary operating environment is only an example of an operatingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of operating environment architecture.Neither should the operating environment be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 2101. The components of thecomputer 2101 can comprise, but are not limited to, one or moreprocessors or processing units 2103, a system memory 2112, and a systembus 2113 that couples various system components including the processor2103 to the system memory 2112. In the case of multiple processing units2103, the system can utilize parallel computing.

The system bus 2113 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures can comprise an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, an AcceleratedGraphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI),a PCI-Express bus, a Personal Computer Memory Card Industry Association(PCMCIA), Universal Serial Bus (USB) and the like. The bus 2113, and allbuses specified in this description can also be implemented over a wiredor wireless network connection and each of the subsystems, including theprocessor 2103, a mass storage device 2104, an operating system 2105,land management software 2106, land management data 2107, a networkadapter 2108, system memory 2112, an Input/Output Interface 2110, adisplay adapter 2109, a display device 2111, and a human machineinterface 2102, can be contained within one or more remote computingdevices 2114 a,b,c at physically separate locations, connected throughbuses of this form, in effect implementing a fully distributed system.

The computer 2101 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 2101 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 2112 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 2112 typically contains data such as land management data2107 and/or program modules such as operating system 2105 and landmanagement software 2106 that are immediately accessible to and/or arepresently operated on by the processing unit 2103.

In another aspect, the computer 2101 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 21 illustrates a mass storage device 2104 whichcan provide non-volatile storage of computer code, computer readableinstructions, data structures, program modules, and other data for thecomputer 2101. For example and not meant to be limiting, a mass storagedevice 2104 can be a hard disk, a removable magnetic disk, a removableoptical disk, magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), and thelike.

Optionally, any number of program modules can be stored on the massstorage device 2104, including by way of example, an operating system2105 and land management software 2106. Each of the operating system2105 and land management software 2106 (or some combination thereof) cancomprise elements of the programming and the land management software2106. Land management data 2107 can also be stored on the mass storagedevice 2104. Land management data 2107 can be stored in any of one ormore databases known in the art. Examples of such databases comprise,DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL,PostgreSQL, and the like. The databases can be centralized ordistributed across multiple systems.

In another aspect, the user can enter commands and information into thecomputer 2101 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the processing unit 2103 via ahuman machine interface 2102 that is coupled to the system bus 2113, butcan be connected by other interface and bus structures, such as aparallel port, game port, an IEEE 1394 Port (also known as a Firewireport), a serial port, or a universal serial bus (USB).

In yet another aspect, a display device 2111 can also be connected tothe system bus 2113 via an interface, such as a display adapter 2109. Itis contemplated that the computer 2101 can have more than one displayadapter 2109 and the computer 2101 can have more than one display device2111. For example, a display device can be a monitor, an LCD (LiquidCrystal Display), or a projector. In addition to the display device2111, other output peripheral devices can comprise components such asspeakers (not shown) and a printer (not shown) which can be connected tothe computer 2101 via Input/Output Interface 2110. Any step and/orresult of the methods can be output in any form to an output device.Such output can be any form of visual representation, including, but notlimited to, textual, graphical, animation, audio, tactile, and the like.The display 2111 and computer 2101 can be part of one device, orseparate devices.

The computer 2101 can operate in a networked environment using logicalconnections to one or more remote computing devices 2114 a,b,c. By wayof example, a remote computing device can be a personal computer,portable computer, smartphone, a server, a router, a network computer, apeer device or other common network node, and so on. Logical connectionsbetween the computer 2101 and a remote computing device 2114 a,b,c canbe made via a network 2115, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be througha network adapter 2108. A network adapter 2108 can be implemented inboth wired and wireless environments. Such networking environments areconventional and commonplace in dwellings, offices, enterprise-widecomputer networks, intranets, and the Internet.

For purposes of illustration, application programs and other executableprogram components such as the operating system 2105 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 2101, and are executed by the data processor(s)of the computer. An implementation of land management software 2106 canbe stored on or transmitted across some form of computer readable media.Any of the disclosed methods can be performed by computer readableinstructions embodied on computer readable media. Computer readablemedia can be any available media that can be accessed by a computer. Byway of example and not meant to be limiting, computer readable media cancomprise “computer storage media” and “communications media.” “Computerstorage media” comprise volatile and non-volatile, removable andnon-removable media implemented in any methods or technology for storageof information such as computer readable instructions, data structures,program modules, or other data. Exemplary computer storage mediacomprises, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by acomputer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, by a computingdevice, first information associated with an environmental managementzone, wherein the first information relates to one or more of a landcharacteristic and a management practice, and wherein the firstinformation comprises a soil type of the environmental management zone;receiving, by the computing device, historical weather data relating tothe environmental management zone; receiving, by the computing device,real-time weather data relating to the environmental management zone;and executing a growth model to predict a nitrogen range for theenvironmental management zone based on one or more of the firstinformation, the historical weather data, and the real-time weatherdata, wherein the nitrogen range comprises probabilities for one or moreof a current time period and a future time period in a growing season.2. The method of claim 1, wherein the growth model predicts the nitrogenrange for the environmental management zone based on second informationcomprising one or more of a seed planting date, a seed planting density,and seed variety attributes.
 3. The method of claim 2, wherein thesecond information relates to maize seed.
 4. The method of claim 1,wherein the growth model is a Crop Environment Resource Synthesis(CERES) maize crop growth and development model.
 5. The method of claim1, wherein the growth model is used to calculate an optimum point of anitrogen response curve based on the probabilities.
 6. The method ofclaim 5, wherein the optimum point of the nitrogen response curve isdetermined based upon a maximum yield per nitrogen.
 7. The method ofclaim 5, wherein the optimum point of the nitrogen response curve isdetermined based upon a maximum yield per nitrogen adjusted for aneconomic cost of one or more sources of nitrogen.
 8. The method of claim5, wherein the optimum point of the nitrogen response curve isdetermined based upon a maximum yield per nitrogen adjusted to minimizerunoff of excess nitrogen from the environmental management zone.
 9. Themethod of claim 5, wherein the growth model further calculates adifference between the optimum point of the nitrogen response curve andone or more of an actual current soil rate of nitrogen and a predictedcurrent soil rate of nitrogen.
 10. The method of claim 5, furthercomprising storing the optimum point in an electronic data file andusing the optimum point to control application of a nitrogen source. 11.The method of claim 1, wherein the growth model comprises a plant growthmodel.
 12. The method of claim 1, wherein executing the growth model topredict the nitrogen range for the environmental management zonecomprises forecasting future weather conditions based on at least one ofthe real-time weather data and the historical weather data.
 13. A methodcomprising: receiving, by a computing device, first informationassociated with an environmental management zone, wherein the firstinformation relates to one or more of a land characteristic and amanagement practice, and wherein the first information comprises a soiltype of the environmental management zone; receiving, by the computingdevice, historical weather data relating to the environmental managementzone; receiving, by the computing device, real-time weather datarelating to the environmental management zone; generating a nitrogenoutcome probability for the environmental management zone based on oneor more of the first information, the historical weather data, and thereal-time weather data for a particular time period; receiving, by thecomputing device, second information associated with the environmentalmanagement zone, wherein the second information comprises a change tothe first information; and updating the nitrogen outcome probabilitybased on the second information.
 14. The method of claim 13, wherein thefirst information comprises one or more of seed planting date, seedplanting density, and seed variety attributes.
 15. The method of claim13, wherein the first information relates to maize seed.
 16. The methodof claim 13, wherein the nitrogen outcome probability is generated usinga Crop Environment Resource Synthesis (CERES) maize crop growth anddevelopment model.
 17. The method of claim 13, wherein the nitrogenoutcome probability comprises a nitrogen response curve, and furthercomprising calculating an optimum point of the nitrogen response curve.18. The method of claim 17, wherein the optimum point of the nitrogenresponse curve is determined based upon a maximum yield per nitrogen.19. The method of claim 17, wherein the optimum point of the nitrogenresponse curve is determined based upon a maximum yield per nitrogenadjusted for an economic cost of one or more sources of nitrogen. 20.The method of claim 17, wherein the optimum point of the nitrogenresponse curve is determined based upon a maximum yield per nitrogenadjusted to minimize runoff of excess nitrogen from the environmentalmanagement zone.
 21. The method of claim 24, wherein the nitrogenoutcome probability is generated based on a growth model configured tocalculate a difference between the optimum point of the nitrogenresponse curve and one or more of an actual current soil rate ofnitrogen and a predicted current soil rate of nitrogen.
 22. The methodof claim 17, wherein generating the nitrogen outcome probability for theenvironmental management zone comprises forecasting future weatherconditions based on at least one of the real-time weather data and thehistorical weather data.
 23. The method of claim 1, wherein the nitrogenrange is expressed as a visually distinct area adjacent to a lineindicating a predicted nitrogen level.
 24. The method of claim 23,wherein the visually distinct area is a shaded area.
 25. The method ofclaim 13, wherein the nitrogen outcome probability is expressed as avisually distinct area adjacent to a line indicating a predictednitrogen level.
 26. The method of claim 25, wherein the visuallydistinct area is a shaded area.
 27. A method of graphically representinga nitrogen range comprising probabilities for a future time period,wherein the probability of said outcomes are graphically represented asa visually distinct area adjacent to a line indicating a predictednitrogen level.
 28. The method of claim 27, wherein the visuallydistinct area is a shaded area.